It is known to provide fluid sampling devices using optical near-field imaging as disclosed in U.S. Pat. No. 5,572,320, which is incorporated herein by reference. Such a device is employed to determine the quantity, size, characteristics, and types of particulate matter in fluids. Examples of fluids which are monitored in such a system are lubricating oils used in engines and rotating machinery; hydraulic fluid used in various machinery; and fluids used in industrial quality control, food processing, medical analysis, and environment control. In its most common use, such a device monitors engine oil for metal particulates or flakes, wherein a size, number, and shape of particulates correspond to an engine condition and can alert one to particular problems with the engine. Non-metallic debris in the fluid can also be detected, such as fibers, sand, dirt and rust particles. Predicting failure is critically important in aircraft engines to avoid accidents and loss of life.
The early stages of engine wear cause small particulate matter, of about 50 microns or less in size, to be generated. These particulates have characteristic shapes indicative of the type of wear produced by specific wear mechanisms. As the wear process progresses, the amount and size of particulates increase. Accordingly, imaging and identifying smaller particles allows early identification of faults, thus, allowing more time for corrective maintenance and preventing unexpected catastrophic failures.
The advantage of the aforementioned system over previous systems is readily apparent when one considers that the previous systems only measured the amount of light passing through the material-laden oil, but gave no consideration as to the particular shape of the material. As best seen in FIGS. 1A–G, the various types of images rendered by a known system can provide a clear indication of the types of problems that are likely to occur based upon the shape and structure of the debris monitored. For example, in FIG. 1A, sliding wear particles are shown and these particles are believed to be caused by metal-to-metal contact due to overloading, misalignment, high speed and/or low oil viscosity. The debris shown in FIG. 1B represents fatigue wear particles which are gear or bearing pieces generated due to surface stress factors such as excessive load, contamination, and the like. FIG. 1C shows cutting wear particles that are generated by surface gouging, two body cutting due to break-in, misalignment, and three body cutting due to particle abrasion. FIG. 1D shows oxide particles which are caused by contamination, and red oxide caused by water or insufficient lubrication of the subject machinery.
It will also be appreciated that certain elements may be in the oil that generate false readings. These elements are classified and may be disregarded by the imaging system. For example, as shown in FIG. 1E, fibers are shown which are normally occurring or may be caused by improper sample handling. In particular, fibers can be from mishandling the fluid which generate false readings. But, valid readings of fibers may be indicative of problems in the system. For example, a filter or composite bearing may be disintegrating. In any event, occurrences of fibers are monitored. Instrument problems due to incomplete removal of air bubbles are represented in FIG. 1F. Finally, FIG. 1G shows flow lines which are a result of instrument problems caused by insufficient replacement of a new sample.
Known tribological debris analysis systems consist of a fluid sample that is connected to a pumping device. The pump is actuated and the fluid is drawn through an optical flow cell which is illuminated by laser light. A discrete input/output board connected to a dedicated computer system controls operation of the pump and the laser in a coordinated manner. An analog camera positioned opposite the laser light obtains an analog video image of particles passing through the optical flow cell. The dedicated computer system processes the analog video by sending the video signal to a digitizer which converts the signal to a digital image. The computer system processes the digital image to determine the shape and size of the particles rendered by the system. About ninety percent of the computer system's processing time is dedicated to pixel level processing associated with the analysis of an image and the detection of object elements. Accordingly, the system requires that the raw video input be directly sent to the general purpose computer for processing and analyzing of the images. It has been found that the known system is quite expensive and easily overloaded. Since the computer system is a dedicated device, it is limited in its ability to analyze the particles and detect any trends associated with the particles. Moreover, the known computer system is unable to check the lifetime history of a particular device when periodic samples are taken from the device. Therefore, such prior art systems, although effective, are not easily adapted for large scale use and implementation.